Large Language Models in Medicine: Applications, Challenges, and Future Directions.

Area of Science:

• Artificial Intelligence in Medicine
• Natural Language Processing
• Healthcare Technology

Background:

• Large language models (LLMs), exemplified by GPT-4, have advanced rapidly in natural language processing tasks.
• The medical field, with its complex data and processes, presents significant opportunities for LLM applications.
• LLMs are increasingly integrated into clinical practice, medical research, and education.

Purpose of the Study:

• To provide a comprehensive overview of LLM applications in medicine.
• To identify and discuss the challenges associated with implementing LLMs in healthcare.
• To explore future directions for the development and integration of medical LLMs.

Main Methods:

• Literature review of recent advancements in LLMs within the medical domain.
• Analysis of current applications across clinical practice, research, and education.
• Identification of key challenges including hallucination, interpretability, and ethical concerns.

Main Results:

• LLMs demonstrate transformative potential in various medical applications.
• Significant challenges hinder the widespread adoption of medical LLMs, including accuracy and ethical considerations.
• Future research should focus on standardized evaluation, multimodal capabilities, and interdisciplinary collaboration.

Conclusions:

• LLMs offer substantial benefits for the medical field, improving efficiency and potential outcomes.
• Addressing challenges such as hallucination and ethical concerns is crucial for reliable medical LLM deployment.
• Continued research and collaboration are essential to fully realize the potential of LLMs in global healthcare transformation.